PEFTEC 2017 - Abstract

A new technique for detecting gas emissions and estimating the locations and mass emission rates of sources.

Abstract Type:

Oral

Session Choice:

Emerging technologies

Presenter Name:

Dr Bill Hirst

Co-authors:

Dr David Randell

Company/Organisation:

Shell Global Solutions International B.V.

Country:

Netherlands

Abstract Information :

We present a detailed description and experimental results for a new gas emission survey
method that remotely detects and maps the locations of multiple gas emission sources distributed
across an extensive area. Our method, which we call LightSource, not only localizes but also
estimates the mass emission rates of each of the detected sources. The approach is generally
applicable to any airborne contaminant that is passively transported and conserved during
transport within the area of interest. We have applied this approach to methane and present
results from an experimental evaluation of its performance using 17 calibrated releases and a
novel optical beam gas sensor, which overcomes the inherent limitations of open-path gas
sensors based on Beer's law absorption behaviour.

Two novel aspects of our approach are: our solution of the associated inverse gas dispersion
problem using Monte-Carlo Markov Chain techniques within a fully Bayesian framework (this
provides us with confidence bounds on all results such as location and mass emission rate; the
second is our adoption of a Laser Dispersion Spectrometer (LDS), for our open-path gas sensor.
This offers dramatic improvements in sensor precision, beam length, speed of measurement and
immunity to common sources of measurement noise: such as the confusion of optical
transmission variations (from beam mis-alignments, rain, fog, snow) with concentration
changes. Our experimental data set comprise path averaged gas concentrations along each of 7
optical beams that are sequentially steered on a timescale of ˜1Hz. Simultaneously we acquire
3D ultrasonic anemometry data and use this and the derived turbulence intensities to drive a
simple Draxler Gaussian plume eddy dispersion model. Mass emission rates are measured by
logging the weight of our gas cylinders; all gas releases were from 2x2m diffuse area sources on
the ground, which were located up to 100m from the sensor, gas release rates were in the range
of 1-2kg/hr.

Our LightSource analysis method solves the inverse problem of where sources are and what
emission rates are required to best explain the data. We use a Gaussian plume atmospheric eddy
dispersion model as our forward model; this provides an ensemble average of expected
dispersion for steady state passive releases. The low concentration gas (hence no buoyancy
effects) is passively transported by advection and diluted by turbulence in the atmosphere, which
is a stochastic process. Effective dispersion prediction is fundamentally dependent on good
measurements of: wind direction, wind speed and turbulence as well as high precision
concentration data. We require sufficient data to adequately reflect the steady state plume and
hence be compatible with the steady-state ensemble average for concentration predicted by the
dispersion model.

It is important that our dispersion model is in closed form - and hence simple- as it operates
within our Markov Chain Monte Carlo (MCMC) scheme and the computational complexity must
be kept manageable. It is also debatable just how much better more complex models perform in
practice, given the underlying stochastic nature of atmospheric dispersion and limited knowledge
available on the 3D trajectory of the gas.

The advantages of this approach over tomographic approaches proposed by others, are that the
required instrumentation is simpler, the volumes of data lower, and sources outside the beam
array are not excluded. Our experimental results demonstrate that we can distinguish and
usefully estimate the individual mass emission rates of single and multiple sources both within
and outside the beam array based on surprisingly limited changes in windspeed and direction.